Graph-based denoising for time-varying point clouds

Abstract

Noisy 3D point clouds arise in many applications. They may be due to errors when constructing a 3D model from images or simply to imprecise depth sensors. Point clouds can be given geometrical structure using graphs created from the similarity information between points. This paper introduces a technique that uses this graph structure and convex optimization methods to denoise 3D point clouds. A short discussion presents how those methods naturally generalize to time-varying inputs such as 3D point cloud time series.

4 pages, 3 figures, 3DTV-Con 2015

References in corpus (1)

• The Emerging Field of Signal Processing on Graphs: Extending High-Dimensional Data Analysis to Networks and Other Irregular Domains

Cited by in corpus (7)

• Score-Based Point Cloud Denoising
• Differentiable Manifold Reconstruction for Point Cloud Denoising
• Feature Graph Learning for 3D Point Cloud Denoising
• Hypergraph Spectral Analysis and Processing in 3D Point Cloud
• High Efficiency Wiener Filter-based Point Cloud Quality Enhancement for MPEG G-PCC
• Dynamic Point Cloud Denoising via Manifold-to-Manifold Distance
• Fast graph-based denoising for point cloud color information